As a major part of beyond 5G (B5G) or 6G network scenarios, autonomous urban aerial mobility (UAM) systems are widely and actively discussed by industry and academia [1]}. Based on these huge interests, many research contributions are available nowadays in terms of UAM trajectory optimization [2]}, energy-efficient operations [3]}, and so forth.
The trajectory optimization and energy-efficient operations fundamentally control the mobility of autonomous UAM systems.
Therefore, conducting visual simulations with the proposed learning-based trajectory optimization and energy-efficient operation algorithms is essentially required in order to intuitively understand the behaviors of the UAM algorithms.
In addition, the most of trajectory optimization algorithms are designed via deep reinforcement learning (DRL) because DRL algorithms are fundamentally for sequential stochastic decision making in order to maximize cumulative expected rewards. Therefore, the UAM simulations should efficiently identify the DRL-based autonomous UAM flying trajectories and operations, and thus, it is obvious that visual representation of the UAM simulations is helpful for intuitive understanding the algorithms.
